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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**大型语言模型（LLMs）** 可以通过工具调用功能与外部数据源进行交互。工具调用是一种强大的技术，它允许开发者构建复杂的应用程序，利用LLMs访问、交互和处理数据库、文件和API等外部资源。\n",
    "\n",
    "![tools_use.png](attachment:tools_use.png)\n",
    "\n",
    "\n",
    "- 扩展语言模型的能力范围:\n",
    "工具调用使大型语言模型能够与外部世界进行交互,极大地扩展了它们的能力范围。它们不再局限于基于预训练数据的文本生成,而是可以访问实时信息、执行计算、操作数据库等。\n",
    "\n",
    "- 实现复杂任务自动化:\n",
    "工具调用允许语言模型执行一系列复杂的操作,如查询数据库、调用API、执行计算等。这使得它们能够自动化许多原本需要人工干预的复杂任务流程。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|  Attribute   | Type |\tDescription  | \n",
    "|  ----  | ----  | ---- |\n",
    "| name  | str |  Must be unique within a set of tools provided to an LLM or agent. |\n",
    "| description  | str | Describes what the tool does. Used as context by the LLM or agent. |\n",
    "| args_schema  | Pydantic BaseModel |  Optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters |\n",
    "| return_direct  | boolean |  Only relevant for agents. When True, after invoking the given tool, the agent will stop and return the result direcly to the user. |\n",
    "\n",
    "\n",
    "\n",
    "各服务提供商已经在其模型中引入了本地工具调用功能。在实际应用中，当LLM为提示提供自动补全时，除了普通文本之外，还可以返回工具调用的列表。OpenAI大约一年前首次发布了“函数调用”，到11月已发展为“工具调用”。此后，其他模型提供商也纷纷效仿：Gemini（12月）、Mistral（2月）、Fireworks（3月）、Together（3月）、Groq（4月）、Cohere（4月）和Anthropic（4月）。\n",
    "\n",
    "\n",
    "大型语言模型可以通过工具调用功能与外部数据源进行交互。工具调用是一种强大的技术,它允许开发者构建复杂的应用程序,利用大型语言模型来访问、交互和操作外部资源,如数据库、文件和API。\n",
    "各大AI服务提供商已经开始在他们的模型中引入原生的工具调用能力。在实践中,这意味着当大型语言模型对提示(prompt)进行自动补全时,除了普通文本之外,还可以返回一系列工具调用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了让聊天模型能够使用工具，我们需要向模型提供一份可用工具的清单，以及这些工具的参数结构。不同的模型提供商可能要求不同的工具定义格式。例如，OpenAI需要的是一个包含“name”（名称）、“description”（描述）和“parameters”（参数）键的字典，而Anthropic则需要“name”（名称）、“description”（描述）和“input_schema”（输入架构）。\n",
    "\n",
    "ChatModel.bind_tools方法提供了一个标准的接口，所有支持调用工具的模型都会实现这个接口。通过这个接口，你可以指定模型能够使用的工具。你可以传递一个工具的原始定义（一个字典），也可以传递可以从其中生成工具定义的其它对象，比如Pydantic类、LangChain工具或任意的函数。这样做使得创建通用的工具定义变得简单，这些定义可以与任意支持调用工具的模型一起使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from getpass import getpass\n",
    "import os\n",
    "\n",
    "DASHSCOPE_API_KEY = getpass()\n",
    "os.environ[\"DASHSCOPE_API_KEY\"] = DASHSCOPE_API_KEY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='我喜欢LLM。', response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'stop', 'request_id': '8d993ca8-df9c-924e-9b2e-e58e5cefc5b0', 'token_usage': {'input_tokens': 29, 'output_tokens': 4, 'total_tokens': 33}}, id='run-393f371a-b80a-4a91-bd9b-ec704b313f83-0')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_community.chat_models.tongyi import ChatTongyi\n",
    "from langchain_core.messages import HumanMessage, SystemMessage\n",
    "\n",
    "chatLLM = ChatTongyi(\n",
    "    streaming=False ,\n",
    ")\n",
    "messages = [\n",
    "    SystemMessage(\n",
    "        content=\"You are a helpful assistant that translates English to Chinese.\"\n",
    "    ),\n",
    "    HumanMessage(\n",
    "        content=\"I love LLM.\"\n",
    "    ),\n",
    "]\n",
    "chatLLM.invoke(messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
    "from langchain_core.tools import tool\n",
    "\n",
    "# ✅ Pydantic class\n",
    "class multiply(BaseModel):\n",
    "    \"\"\"Return product of 'x' and 'y'.\"\"\"\n",
    "    x: float = Field(..., description=\"First factor\")\n",
    "    y: float = Field(..., description=\"Second factor\")\n",
    "    \n",
    "# ✅ LangChain tool\n",
    "@tool\n",
    "def exponentiate(x: float, y: float) -> float:\n",
    "    \"\"\"Raise 'x' to the 'y'.\"\"\"\n",
    "    return x**y\n",
    "    \n",
    "# ✅ Function\n",
    "def subtract(x: float, y: float) -> float:\n",
    "    \"\"\"Subtract 'x' from 'y'.\"\"\"\n",
    "    return y-x\n",
    "    \n",
    "# ✅ OpenAI-format dict\n",
    "# Could also pass in a JSON schema with \"title\" and \"description\" \n",
    "add = {\n",
    "  \"name\": \"add\",\n",
    "  \"description\": \"Add 'x' and 'y'.\",\n",
    "  \"parameters\": {\n",
    "    \"type\": \"object\",\n",
    "    \"properties\": {\n",
    "      \"x\": {\"type\": \"number\", \"description\": \"First number to add\"},\n",
    "      \"y\": {\"type\": \"number\", \"description\": \"Second number to add\"}\n",
    "    },\n",
    "    \"required\": [\"x\", \"y\"]\n",
    "  }\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtract(x=5,y=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.multiply"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Whenever we invoke `llm_with_tool`, all three of these tool definitions\n",
    "# are passed to the model.\n",
    "llm_with_tools = chatLLM.bind_tools([multiply, exponentiate, add, subtract])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tools': [{'type': 'function',\n",
       "   'function': {'name': 'multiply',\n",
       "    'description': \"Return product of 'x' and 'y'.\",\n",
       "    'parameters': {'type': 'object',\n",
       "     'properties': {'x': {'description': 'First factor', 'type': 'number'},\n",
       "      'y': {'description': 'Second factor', 'type': 'number'}},\n",
       "     'required': ['x', 'y']}}},\n",
       "  {'type': 'function',\n",
       "   'function': {'name': 'exponentiate',\n",
       "    'description': \"Raise 'x' to the 'y'.\",\n",
       "    'parameters': {'type': 'object',\n",
       "     'properties': {'x': {'type': 'number'}, 'y': {'type': 'number'}},\n",
       "     'required': ['x', 'y']}}},\n",
       "  {'type': 'function',\n",
       "   'function': {'name': 'add',\n",
       "    'description': \"Add 'x' and 'y'.\",\n",
       "    'parameters': {'type': 'object',\n",
       "     'properties': {'x': {'type': 'number',\n",
       "       'description': 'First number to add'},\n",
       "      'y': {'type': 'number', 'description': 'Second number to add'}},\n",
       "     'required': ['x', 'y']}}},\n",
       "  {'type': 'function',\n",
       "   'function': {'name': 'subtract',\n",
       "    'description': \"Subtract 'x' from 'y'.\",\n",
       "    'parameters': {'type': 'object',\n",
       "     'properties': {'x': {'type': 'number'}, 'y': {'type': 'number'}},\n",
       "     'required': ['x', 'y']}}}]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_with_tools.kwargs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"COHERE_API_KEY\"] = \"3Vi6ldSECOJIHHrFRZxTZQb6GVH1qqqXD3PpZF7H\"\n",
    "\n",
    "from langchain_cohere import ChatCohere\n",
    "\n",
    "llm = ChatCohere(model=\"command-r\")\n",
    "\n",
    "llm_with_tools_cohere = llm.bind_tools([multiply, exponentiate, subtract])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tools': [{'name': 'multiply',\n",
       "   'description': \"Return product of 'x' and 'y'.\",\n",
       "   'parameter_definitions': {'x': {'description': 'First factor',\n",
       "     'type': 'float',\n",
       "     'required': True},\n",
       "    'y': {'description': 'Second factor', 'type': 'float', 'required': True}}},\n",
       "  {'name': 'exponentiate',\n",
       "   'description': \"Raise 'x' to the 'y'.\",\n",
       "   'parameter_definitions': {'x': {'description': '',\n",
       "     'type': 'float',\n",
       "     'required': True},\n",
       "    'y': {'description': '', 'type': 'float', 'required': True}}},\n",
       "  {'name': 'subtract',\n",
       "   'description': \"Subtract 'x' from 'y'.\",\n",
       "   'parameter_definitions': {'x': {'type': 'float',\n",
       "     'required': True,\n",
       "     'description': None},\n",
       "    'y': {'type': 'float', 'required': True, 'description': None}}}]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_with_tools_cohere.kwargs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"What is 3 * 12? \"\n",
    "tools_result = llm_with_tools.invoke(query).tool_calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'multiply', 'args': {'x': 3, 'y': 12}, 'id': ''}\n"
     ]
    }
   ],
   "source": [
    "for tool_call in tools_result:\n",
    "    print(tool_call)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "type object 'multiply' has no attribute 'invoke'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m selected_tool \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmultiply\u001b[39m\u001b[38;5;124m\"\u001b[39m: multiply , \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexponentiate\u001b[39m\u001b[38;5;124m\"\u001b[39m: exponentiate , \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124madd\u001b[39m\u001b[38;5;124m\"\u001b[39m:add , \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msubtract\u001b[39m\u001b[38;5;124m\"\u001b[39m: subtract}[tool_call[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mlower()]\n\u001b[0;32m----> 2\u001b[0m \u001b[43mselected_tool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m(tool_call[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
      "\u001b[0;31mAttributeError\u001b[0m: type object 'multiply' has no attribute 'invoke'"
     ]
    }
   ],
   "source": [
    "selected_tool = {\"multiply\": multiply , \"exponentiate\": exponentiate , \"add\":add , \"subtract\": subtract}[tool_call[\"name\"].lower()]\n",
    "selected_tool.invoke(tool_call[\"args\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "subtract() missing 1 required positional argument: 'y'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msubtract\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_call\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43margs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: subtract() missing 1 required positional argument: 'y'"
     ]
    }
   ],
   "source": [
    "subtract(tool_call[\"args\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x': 3, 'y': 12}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tool_call[\"args\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.multiply"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [HumanMessage(query)]\n",
    "ai_msg = llm_with_tools.invoke(messages)\n",
    "messages.append(ai_msg)\n",
    "for tool_call in ai_msg.tool_calls:\n",
    "    selected_tool = {\"add\": add, \"multiply\": multiply}[tool_call[\"name\"].lower()]\n",
    "    tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
    "    messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
    "messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "AIMessage.tool_calls 是一个在工具调用模型中使用的标准化接口，用于获取模型的工具调用信息。在使用工具调用模型时，模型返回的工具调用信息可能会出现在 AIMessage.additional_kwargs 或 AIMessage.content 中，具体取决于模型提供者的 API，并且遵循提供者特定的格式。这意味着您需要自定义逻辑来从不同模型的输出中提取工具调用信息。现在，AIMessage.tool_calls 提供了一个标准化接口，用于获取模型工具调用信息。因此，在使用绑定工具调用模型后，您将获得一个包含工具调用信息的输出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'multiply', 'args': {'x': 3, 'y': 12}, 'id': ''}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"What is 3 * 12? Also, what is 11-49?\"\n",
    "\n",
    "llm_with_tools.invoke(query).tool_calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'multiply',\n",
       "  'args': {'x': 3, 'y': 12},\n",
       "  'id': 'ebbdffb1e4274564bd2332007332db58'},\n",
       " {'name': 'subtract',\n",
       "  'args': {'x': 11, 'y': 49},\n",
       "  'id': 'f40a96fbced04fc49fcbf5b0d59c1e3d'}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_with_tools_cohere.invoke(query).tool_calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "content='' additional_kwargs={'tool_calls': [{'function': {'name': 'multiply', 'arguments': '{\"first_int\": 5, \"second_int\": 45}'}, 'id': '', 'type': 'function'}]} response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'tool_calls', 'request_id': 'f488d304-6886-981b-9dcc-97e0c762e96b', 'token_usage': {'input_tokens': 186, 'output_tokens': 25, 'total_tokens': 211}} id='run-8cdf08c1-562c-43fd-bd14-ab1a94ecea82-0' tool_calls=[{'name': 'multiply', 'args': {'first_int': 5, 'second_int': 45}, 'id': ''}]\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.chat_models.tongyi import ChatTongyi\n",
    "from langchain_core.tools import tool\n",
    "\n",
    "\n",
    "@tool\n",
    "def multiply(first_int: int, second_int: int) -> int:\n",
    "    \"\"\"Multiply two integers together.\"\"\"\n",
    "    return first_int * second_int\n",
    "\n",
    "\n",
    "llm = ChatTongyi(model=\"qwen-turbo\")\n",
    "\n",
    "llm_with_tools = llm.bind_tools([multiply])\n",
    "\n",
    "msg = llm_with_tools.invoke(\"5乘以45是多少？\")\n",
    "\n",
    "print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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